Making Decisions in the Era of the Clinical Decision Rule: How Emergency Physicians Use Clinical Decision Rules
Bibliographic record
Abstract
<p>Purpose: Physicians are often asked to integrate clinical decision rules (CDRs) with their own cognitive processes to reach a diagnosis. Clinicians, researchers, and educators must understand these cognitive processes to evaluate and improve the diagnostic process. The authors sought to explore emergency physicians' diagnostic processes and to examine how they integrated CDRs into their reasoning using simulated cases (with chest pain or leg pain).</p> <p>Method: From August 2015 to July 2016, 16 practicing emergency physicians from 3 teaching hospitals associated with McMaster University, Ontario, Canada, were interviewed via a novel "teach aloud" protocol. Six videos of simulated patients with chest pain, breathlessness, or leg discomfort were used as prompts for the physicians to demonstrate their diagnostic thinking. Using a constructivist grounded theory analysis, 3 investigators independently reviewed the interview transcripts, meeting regularly to discuss identified themes and subthemes until sufficiency was reached.</p> <p>Results: A model to describe how clinicians integrate their own decision making with CDRs was developed, showing that physicians engage in an iterative diagnostic process that repeatedly refines the differential diagnosis list. The steps in the diagnostic process were: refinement of the differential diagnosis, ordering a hierarchy of risk, the decision to test, choosing the tests, and interpreting test results. Physicians applied CDRs when they had already decided to test.</p> <p>Conclusions: To date, CDRs assume a static, linear model of clinical decision making. Findings demonstrate that participants engaged in iterative and dynamic decision-making processes that changed throughout their patient encounter, contingent on multiple contextual features. Understanding these processes could inform future development of CDRs and educational strategies around these decision aids.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".